Simultaneous supervised clustering and feature selection over a graph

• 成果类型: Article
• 署名作者: Shen, Xiaotong; Huang, Hsin-Cheng; Pan, Wei
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; Academia Sinica - Taiwan; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass038
• 发表日期: 2012
• 页码: 899914
• 关键词: VARIABLE SELECTION; regularization; regression

摘要

In this article, we propose a regression method for simultaneous supervised clustering and feature selection over a given undirected graph, where homogeneous groups or clusters are estimated as well as informative predictors, with each predictor corresponding to one node in the graph and a connecting path indicating a priori possible grouping among the corresponding predictors. The method seeks a parsimonious model with high predictive power through identifying and collapsing homogeneous groups of regression coefficients. To address computational challenges, we present an efficient algorithm integrating the augmented Lagrange multipliers, coordinate descent and difference convex methods. We prove that the proposed method not only identifies the true homogeneous groups and informative features consistently but also leads to accurate parameter estimation. A gene network dataset is analysed to demonstrate that the method can make a difference by exploring dependency structures among the genes.